ArticleBMC genomics2016
VarElect: the phenotype-based variation prioritizer of the GeneCards Suite.
Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 157 papers, 1 of them a synthesis that pooled it.
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Who cites it
157 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- GRASPing experience-dependent protein expression signatures enriched for hippocampal engram cell synapses.Science advances · 2026Article
- Progressive abnormal gait in an adult Jack Russell Terrier with a homozygous frameshift variant in SETX (senataxin).Journal of veterinary internal medicine · 2026Article
- PhenoSS: Phenotype semantic similarity-based approach for rare disease prediction and patient clustering.medRxiv : the preprint server for health sciences · 2026Article
- X-Linked Muscular Dystrophy in a Cat with a Putative Variant in theAnimals : an open access journal from MDPI · 2026Article
- HSP90AA1 Facilitates Vascular Calcification in Chronic Kidney Disease Involving Chaperone-Mediated Autophagy.Biomedicines · 2026Article
- Consensus-based Detection of Aetiologic Copy Number Variants For Syndromic Orofacial Clefts Utilising Whole Exome Sequencing of Case Parent Trios.Research square · 2026Article
- Genetic risk factor identification for common epilepsies guided by integrative omics data analysis.Epilepsia · 2026Article
- Sesamin alleviates androgenetic alopecia by targeting the AR MAPK Wnt axis.Scientific reports · 2026Article
- Seminal long non-coding RNAs as prognostic non-invasive biomarkers in non-obstructive azoospermia.BMC medical genomics · 2026Article
- Multi-omics analysis and functional validation of CHEK1 as an independent prognostic biomarker in Pancreatic cancer.PloS one · 2026Article
- Elucidating the molecular mechanisms of paeoniflorin intervention in oral lichen planus: a computational biology and bioinformatics-based research strategy.Frontiers in bioinformatics · 2026Article
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- Network-Based Identification ofCurrent pharmaceutical design · 2026Article
97 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
21 authors.
Funding
Abstract
backgroundNext generation sequencing (NGS) provides a key technology for deciphering the genetic underpinnings of human diseases. Typical NGS analyses of a patient depict tens of thousands non-reference coding variants, but only one or very few are expected to be significant for the relevant disorder. In a filtering stage, one employs family segregation, rarity in the population, predicted protein impact and evolutionary conservation as a means for shortening the variation list. However, narrowing down further towards culprit disease genes usually entails laborious seeking of gene-phenotype relationships, consulting numerous separate databases. Thus, a major challenge is to transition from the few hundred shortlisted genes to the most viable disease-causing candidates.
resultsWe describe a novel tool, VarElect ( http://ve.genecards.org ), a comprehensive phenotype-dependent variant/gene prioritizer, based on the widely-used GeneCards, which helps rapidly identify causal mutations with extensive evidence. The GeneCards suite offers an effective and speedy alternative, whereby >120 gene-centric automatically-mined data sources are jointly available for the task. VarElect cashes on this wealth of information, as well as on GeneCards' powerful free-text Boolean search and scoring capabilities, proficiently matching variant-containing genes to submitted disease/symptom keywords. The tool also leverages the rich disease and pathway information of MalaCards, the human disease database, and PathCards, the unified pathway (SuperPaths) database, both within the GeneCards Suite. The VarElect algorithm infers direct as well as indirect links between genes and phenotypes, the latter benefitting from GeneCards' diverse gene-to-gene data links in GenesLikeMe. Finally, our tool offers an extensive gene-phenotype evidence portrayal ("MiniCards") and hyperlinks to the parent databases.
conclusionsWe demonstrate that VarElect compares favorably with several often-used NGS phenotyping tools, thus providing a robust facility for ranking genes, pointing out their likelihood to be related to a patient's disease. VarElect's capacity to automatically process numerous NGS cases, either in stand-alone format or in VCF-analyzer mode (TGex and VarAnnot), is indispensable for emerging clinical projects that involve thousands of whole exome/genome NGS analyses.
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